Automated human mind reading using EEG signals for seizure detection

Abstract

Epilepsy is one of the most occurring neurological disease globally emerged back in 4000 BC. It is affecting around 50 million people of all ages these days. The trait of this disease is recurrent seizures. In the past few decades, the treatments available for seizure control have improved a lot with the advancements in the field of medical science and technology. Electroencephalogram (EEG) is a widely used technique for monitoring the brain activity and widely popular for seizure region detection. It is performed before surgery and also to predict seizure at the time operation which is useful in neuro stimulation device. But in most of cases visual examination is done by neurologist in order to detect and classify patterns of the disease but this requires a lot of pre-domain knowledge and experience. This all in turns put a pressure on neurosurgeons and leads to time wastage and also reduce their accuracy and efficiency. There is a need of some automated systems in arena of information technology like use of neural networks in deep learning which can assist neurologists. In the present paper, a model is proposed to give an accuracy of 98.33% which can be used for development of automated systems. The developed system will significantly help neurologists in their performance.

Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management

“…Finally, the abstracts of the remaining 343 articles were screened for relevance towards the aim of this systematic review, where 313 articles were found to be irrelevant as they mainly consisted of surveys/opinions on the medical/technology devices used for management of epilepsy without any utilization of description. Unfortunately, three articles were further removed from the selected articles as they were neither available as a full text nor were in English language [ 45 - 47 ]. Thus, a final total of 27 articles were selected for critical appraisal and were systematically evaluated and included in this review.…”

“…This, together with the 1D Convolution Neural Network model proposed by Ranga and colleagues and the single-step processing chain proposed in the study by Djoufack Nkengfack’s, may highly improve the currently available EEG-based seizure prediction and management devices worldwide, in terms of precision, accuracy, specificity and sensitivity. The 1D Convolution model will enable automated analysis of EEG-signals [ 45 ], while the processing chain involving the Jacobi polynomial transforms and linear discriminant analysis may lead to the development of treatment triggering prediction devices [ 44 ], thus saving time and increasing the efficiency of ambulatory EEG-based devices in preventing seizures in patients.…”

BrainNet: A Unified Neural Network Architecture for Brain Image Classification

“…Clinicians often find it hectic to diagnose multiple diseases simultaneously, rendering treatment tedious and time-consuming. Recent literature has introduced several machine 1 thanks to advancements in medical science learning models to aid clinicians and, in turn, enhance the accuracy of medical systems (Gupta et al (2020); Ranga et al (2020Ranga et al ( , 2022; Gupta et al (2021)). However, the current SOT A architecture is presently constrained in its ability to effectively identify multiple interconnected abnormalities (Kujur et al (2022)).…”